import pandas as pd
import numpy as np
from sklearn.cluster import DBSCAN, AgglomerativeClustering, OPTICS
from sklearn.metrics.cluster import (homogeneity_score, silhouette_score, davies_bouldin_score,
                                     adjusted_rand_score, calinski_harabasz_score, adjusted_mutual_info_score,
                                     v_measure_score, completeness_score)

from sklearn.datasets import make_moons
from collections import defaultdict
import itertools

import matplotlib.pyplot as plt

import warnings
warnings.filterwarnings('ignore')

pd.set_option('display.max.rows', None)

Generate toy dataset¶

dataset = make_moons(n_samples=2000, noise=0.75)

data, labels = dataset

Baseline algorithms¶

clustering_algorithms = (DBSCAN, AgglomerativeClustering, OPTICS)
clustering_algorithms_titles = ('DBSCAN', 'AgglomerativeClustering', 'OPTICS')
clustering_metrics = (homogeneity_score, silhouette_score, davies_bouldin_score, adjusted_rand_score, 
                      calinski_harabasz_score, adjusted_mutual_info_score, v_measure_score, completeness_score)
clustering_metrics_titles = ('homogeneity_score', 'silhouette_score', 'davies_bouldin_score',
                             'adjusted_rand_index', 'calinski_harabasz_score', 'adjusted_mutual_info',
                             'v_measure_score', 'completeness_score'
                            )

Calculate metrics for each algorithm¶

labels_mapping = {}

metrics_dataframe = pd.DataFrame(index=clustering_algorithms_titles, columns=clustering_metrics_titles)

for clustering_algorithm, clustering_algorithms_title in zip(clustering_algorithms, clustering_algorithms_titles):
    algorithm = clustering_algorithm()
    tmp_predictions = algorithm.fit_predict(data)
    
    labels_mapping[clustering_algorithms_title] = tmp_predictions
    
    for metric, metric_title in zip(clustering_metrics, clustering_metrics_titles):
        if metric_title not in ('silhouette_score', 'davies_bouldin_score', 'calinski_harabasz_score'):
            metrics_dataframe.loc[clustering_algorithms_title, metric_title] = metric(labels, tmp_predictions)
        else:
            metrics_dataframe.loc[clustering_algorithms_title, metric_title] = metric(data, labels)

metrics_dataframe.index.name = 'clustering_algorithm'
metrics_dataframe

def highlight_min(data, color='yellow'):
    attr = 'background-color: {}'.format(color)
    data = data.replace('%','', regex=True).astype(float)
    if data.ndim == 1:
        bound = data == data.min() 
        return [attr if row else '' for row in bound]
    else:
        bound = data == data.min().min()  
        return pd.DataFrame(np.where(bound, attr, ''), index=data.index, columns=data.columns)
    
def highlight_max(data, color='yellow'):
    attr = 'background-color: {}'.format(color)
    data = data.replace('%','', regex=True).astype(float)
    if data.ndim == 1:
        bound = data == data.max() 
        return [attr if row else '' for row in bound]
    else:
        bound = data == data.max().max()  
        return pd.DataFrame(np.where(bound, attr, ''), index=data.index, columns=data.columns)

metrics_best_mapping = {'davies_bouldin_score': highlight_min,
                       'calinski_harabasz_score': highlight_max,
                       'silhouette_score': highlight_max,
                       'homogeneity_score': highlight_max,
                       'adjusted_rand_index': highlight_max,
                       'adjusted_mutual_info': highlight_max,
                       'v_measure_score': highlight_max,
                       'completeness_score': highlight_max
                       }

metrics_min = ['davies_bouldin_score']
metrics_max = list(set(clustering_metrics_titles) - set(metrics_min))

Find best algorithms for each metric of clusterization¶

Davies_bouldin_score we have to minimize¶

metrics_dataframe[metrics_min].style.apply(highlight_min)

Other metrics we have to maximize¶

metrics_dataframe[metrics_max].style.apply(highlight_max)

Agglomerative Clustering seems to be the best algorithm, according to clusterization metrics

Vizualize clusters for each method¶

fig, axs = plt.subplots(nrows=len(clustering_algorithms_titles), ncols=1, figsize=(10, 20))

idx = 0

for clustering_algorithm, labels in labels_mapping.items():
    colors_mapping = [(item/255.) for item in labels]
    axs[idx].scatter(data[:, 0], data[:, 1], c=colors_mapping);
    axs[idx].set_title('{}, number of selected clusters: {}'.format(clustering_algorithm, len(set(labels))))
    
    idx += 1

According to visualisation's results we can say that the best algorithm for clustering is Agglomerative Clustering.

Thus we consider that the best metrics for evaluate clustering are v_measure_score, completeness_score, adjusted_mutual_info and adjusted_rand_index.

Experiments with parameters¶

DBSCAN¶

Experiments with min samples, metric and epsilon¶

min_samples_range = range(2, 10)
distance_metrics = ('euclidean', 'l1', 'l2', 'manhattan', 'cosine', 'chebyshev')
epsilons_range = (1e-5, 1e-4, 1e-3, 0.01, 0.1)

dbscan_clustering_labels_mapping = {eps: {metrics: {samples: None for samples in min_samples_range} 
                                     for metrics in distance_metrics} 
                                    for eps in epsilons_range}


index = pd.MultiIndex.from_product((min_samples_range, distance_metrics, epsilons_range))

metrics_dataframe_dbscan = pd.DataFrame(index=index, columns=clustering_metrics_titles)

for min_samples, distance_metric, epsilon in itertools.product(min_samples_range, distance_metrics, epsilons_range):
    algorithm = DBSCAN(min_samples=min_samples, metric=distance_metric, eps=epsilon)
    dbscan_predictions = algorithm.fit_predict(data)
    
    dbscan_clustering_labels_mapping[epsilon][distance_metric][min_samples] = dbscan_predictions
    
    for metric, metric_title in zip(clustering_metrics, clustering_metrics_titles):
        if metric_title not in ('silhouette_score', 'davies_bouldin_score', 'calinski_harabasz_score'):
            metrics_dataframe_dbscan.loc[(min_samples, distance_metric, epsilon), metric_title] = metric(
                labels, dbscan_predictions)
        else:
            metrics_dataframe_dbscan.loc[(min_samples, distance_metric, epsilon), metric_title] = metric(data, labels)

metrics_dataframe_dbscan.index.names = ['min_samples', 'metrics', 'epsilon']
metrics_dataframe_dbscan

Find best parameters for DBSCAN Clustering¶

Davies_bouldin_score we have to minimize¶

metrics_dataframe_dbscan[metrics_min].style.apply(highlight_min)

Other metrics we have to maximize¶

metrics_dataframe_dbscan[metrics_max].style.apply(highlight_max)

Seems like epsilon 0.1 with 4 clusters and L1 or 5 clusters and manhattan metrics are the best for DBSCAN algorithm

OPTICS¶

Experiments with min samples, metric and epsilon¶

min_samples_range = range(2, 10)
distance_metrics = ('euclidean', 'l1', 'l2', 'manhattan', 'cosine', 'chebyshev')

epsilons_range = (1e-5, 1e-4, 1e-3, 0.01, 0.1)

optics_clustering_labels_mapping = {eps: {metrics: {samples: None for samples in min_samples_range} 
                                     for metrics in distance_metrics} 
                                    for eps in epsilons_range}

index = pd.MultiIndex.from_product((min_samples_range, distance_metrics, epsilons_range))

metrics_dataframe_optics = pd.DataFrame(index=index, columns=clustering_metrics_titles)

for min_samples, distance_metric, epsilon in itertools.product(min_samples_range, distance_metrics, epsilons_range):
    algorithm = OPTICS(min_samples=min_samples, metric=distance_metric, eps=epsilon)
    optics_predictions = algorithm.fit_predict(data)
    
    optics_clustering_labels_mapping[epsilon][distance_metric][min_samples] = optics_predictions
    
    for metric, metric_title in zip(clustering_metrics, clustering_metrics_titles):
        if metric_title not in ('silhouette_score', 'davies_bouldin_score', 'calinski_harabasz_score'):
            metrics_dataframe_optics.loc[(min_samples, distance_metric, epsilon), metric_title] = metric(
                labels, optics_predictions)
        else:
            metrics_dataframe_optics.loc[(min_samples, distance_metric, epsilon), metric_title] = metric(data, labels)

metrics_dataframe_optics.index.names = ['min_samples', 'metrics', 'epsilon']
metrics_dataframe_optics

Find best parameters for OPTICS Clustering¶

Davies_bouldin_score we have to minimize¶

metrics_dataframe_optics[metrics_min].style.apply(highlight_min)

Other metrics we have to maximize¶

metrics_dataframe_optics[metrics_max].style.apply(highlight_max)

Seems like 4 clusters and Euclidean or L2 are the best for OPTICS algorithm. Epsilon doesn't influence on change metrics values.

Agglomerative Clustering¶

Experiments with number of clusters and distance metrics¶

clusters_range = range(2, 10)
distance_metrics = ('euclidean', 'l1', 'l2', 'manhattan', 'cosine')

agglomerative_clustering_labels_mapping = defaultdict(dict)

index = pd.MultiIndex.from_product((clusters_range, distance_metrics))

metrics_dataframe_agglomerative = pd.DataFrame(index=index, columns=clustering_metrics_titles)

for nclusters, distance_metric in itertools.product(clusters_range, distance_metrics):
    algorithm = AgglomerativeClustering(n_clusters=nclusters, affinity=distance_metric, linkage='average')
    agglomerative_predictions = algorithm.fit_predict(data)
    
    agglomerative_clustering_labels_mapping[nclusters][distance_metric] = agglomerative_predictions
    
    for metric, metric_title in zip(clustering_metrics, clustering_metrics_titles):
        if metric_title not in ('silhouette_score', 'davies_bouldin_score', 'calinski_harabasz_score'):
            metrics_dataframe_agglomerative.loc[(nclusters, distance_metric), metric_title] = metric(
                labels, agglomerative_predictions)
        else:
            metrics_dataframe_agglomerative.loc[(nclusters, distance_metric), metric_title] = metric(data, labels)

metrics_dataframe_agglomerative.index.names = ['nclusters', 'affinity']
metrics_dataframe_agglomerative

Find best nclusters and affinity for Agglomerative Clustering¶

Davies_bouldin_score we have to minimize¶

metrics_dataframe_agglomerative[metrics_min].style.apply(highlight_min)

Other metrics we have to maximize¶

metrics_dataframe_agglomerative[metrics_max].style.apply(highlight_max)

Seems like 9 clusters and cosine are the best for Agglomerative Clustering algorithm

Vizualize clusters for each parameter¶

fig, axs = plt.subplots(nrows=len(clusters_range), ncols=len(distance_metrics), figsize=(100, 120))

idx = 0

for i, data_agg in enumerate(agglomerative_clustering_labels_mapping.items()):
    nclusters, data_dict = data_agg
    for j, data_agg1 in enumerate(data_dict.items()):
        affinity, labels = data_agg1
        colors_mapping = [(item/255.) for item in labels]
        axs[i][j].scatter(data[:, 0], data[:, 1], c=colors_mapping);
        axs[i][j].set_title('Agglomerative Clustering, number of selected clusters: {}, affinity: {}'.format(
            nclusters, affinity))

Dataset 2¶

dataset for task about heart attack¶

from sklearn.decomposition import PCA

dataset2 = pd.read_csv('heart.csv')

dataset2.head()

dataset_task2 = dataset2.iloc[:,0:-1]
dataset_task2.shape

(297, 13)

labels = dataset2['target']

pca = PCA(n_components=2)

dataset_2d = pca.fit_transform(dataset_task2)

Calculate metrics for each algorithm¶

labels_mapping = {}

metrics_dataframe = pd.DataFrame(index=clustering_algorithms_titles, columns=clustering_metrics_titles)

for clustering_algorithm, clustering_algorithms_title in zip(clustering_algorithms, clustering_algorithms_titles):
    algorithm = clustering_algorithm()
    tmp_predictions = algorithm.fit_predict(dataset_2d)
    
    labels_mapping[clustering_algorithms_title] = tmp_predictions
    
    for metric, metric_title in zip(clustering_metrics, clustering_metrics_titles):
        if metric_title not in ('silhouette_score', 'davies_bouldin_score', 'calinski_harabasz_score'):
            metrics_dataframe.loc[clustering_algorithms_title, metric_title] = metric(labels, tmp_predictions)
        else:
            metrics_dataframe.loc[clustering_algorithms_title, metric_title] = metric(dataset_2d, labels)

metrics_dataframe.index.name = 'clustering_algorithm'
metrics_dataframe

Find best algorithms for each metric of clusterization¶

Davies_bouldin_score we have to minimize¶

metrics_dataframe[metrics_min].style.apply(highlight_min)

Other metrics we have to maximize¶

metrics_dataframe[metrics_max].style.apply(highlight_max)

OPTICS seems to be the best algorithm, according to clusterization metrics

Vizualize clusters for each method¶

fig, axs = plt.subplots(nrows=len(clustering_algorithms_titles), ncols=1, figsize=(10, 20))

idx = 0

for clustering_algorithm, labels in labels_mapping.items():
    colors_mapping = [(item/255.) for item in labels]
    axs[idx].scatter(dataset_2d[:, 0], dataset_2d[:, 1], c=colors_mapping);
    axs[idx].set_title('{}, number of selected clusters: {}'.format(clustering_algorithm, len(set(labels))))
    
    idx += 1

According to this visualisation we can say that the best algorithm is Agglomerative Clustering, such as we know that this task have to have only two clusters.

Thus we consider that the best metrics is adjusted_rand_index.

Experiments with parameters¶

DBSCAN¶

Experiments with min samples, metric and epsilon¶

min_samples_range = range(2, 10)
distance_metrics = ('euclidean', 'l1', 'l2', 'manhattan', 'cosine', 'chebyshev')
epsilons_range = (1e-5, 1e-4, 1e-3, 0.01, 0.1)

dbscan_clustering_labels_mapping = {eps: {metrics: {samples: None for samples in min_samples_range} 
                                     for metrics in distance_metrics} 
                                    for eps in epsilons_range}


index = pd.MultiIndex.from_product((min_samples_range, distance_metrics, epsilons_range))

metrics_dataframe_dbscan = pd.DataFrame(index=index, columns=clustering_metrics_titles)

for min_samples, distance_metric, epsilon in itertools.product(min_samples_range, distance_metrics, epsilons_range):
    algorithm = DBSCAN(min_samples=min_samples, metric=distance_metric, eps=epsilon)
    dbscan_predictions = algorithm.fit_predict(dataset_2d)
    
    dbscan_clustering_labels_mapping[epsilon][distance_metric][min_samples] = dbscan_predictions
    
    for metric, metric_title in zip(clustering_metrics, clustering_metrics_titles):
        if metric_title not in ('silhouette_score', 'davies_bouldin_score', 'calinski_harabasz_score'):
            metrics_dataframe_dbscan.loc[(min_samples, distance_metric, epsilon), metric_title] = metric(
                labels, dbscan_predictions)
        else:
            metrics_dataframe_dbscan.loc[(min_samples, distance_metric, epsilon), metric_title] = metric(dataset_2d, labels)

metrics_dataframe_dbscan.index.names = ['min_samples', 'metrics', 'epsilon']
metrics_dataframe_dbscan

Find best parameters for DBSCAN Clustering¶

Davies_bouldin_score we have to minimize¶

metrics_dataframe_dbscan[metrics_min].style.apply(highlight_min)

Other metrics we have to maximize¶

metrics_dataframe_dbscan[metrics_max].style.apply(highlight_max)

Seems like 2 clusters, manhattan and epsilon 1e-04 metrics are the best for DBSCAN algorithm

OPTICS¶

Experiments with min samples, metric and epsilon¶

min_samples_range = range(2, 10)
distance_metrics = ('euclidean', 'l1', 'l2', 'manhattan', 'cosine', 'chebyshev')

epsilons_range = (1e-5, 1e-4, 1e-3, 0.01, 0.1)

optics_clustering_labels_mapping = {eps: {metrics: {samples: None for samples in min_samples_range} 
                                     for metrics in distance_metrics} 
                                    for eps in epsilons_range}

index = pd.MultiIndex.from_product((min_samples_range, distance_metrics, epsilons_range))

metrics_dataframe_optics = pd.DataFrame(index=index, columns=clustering_metrics_titles)

for min_samples, distance_metric, epsilon in itertools.product(min_samples_range, distance_metrics, epsilons_range):
    algorithm = OPTICS(min_samples=min_samples, metric=distance_metric, eps=epsilon)
    optics_predictions = algorithm.fit_predict(dataset_2d)
    
    optics_clustering_labels_mapping[epsilon][distance_metric][min_samples] = optics_predictions
    
    for metric, metric_title in zip(clustering_metrics, clustering_metrics_titles):
        if metric_title not in ('silhouette_score', 'davies_bouldin_score', 'calinski_harabasz_score'):
            metrics_dataframe_optics.loc[(min_samples, distance_metric, epsilon), metric_title] = metric(
                labels, optics_predictions)
        else:
            metrics_dataframe_optics.loc[(min_samples, distance_metric, epsilon), metric_title] = metric(dataset_2d, labels)

metrics_dataframe_optics.index.names = ['min_samples', 'metrics', 'epsilon']
metrics_dataframe_optics

Find best parameters for OPTICS Clustering¶

Davies_bouldin_score we have to minimize¶

metrics_dataframe_optics[metrics_min].style.apply(highlight_min)

Other metrics we have to maximize¶

metrics_dataframe_optics[metrics_max].style.apply(highlight_max)

Seems like 4 clusters and Euclidean or L2 are the best for OPTICS algorithm. Epsilon doesn't influence on change metrics values.

Agglomerative Clustering¶

Experiments with number of clusters and distance metrics¶

clusters_range = range(2, 10)
distance_metrics = ('euclidean', 'l1', 'l2', 'manhattan', 'cosine')

agglomerative_clustering_labels_mapping = defaultdict(dict)

index = pd.MultiIndex.from_product((clusters_range, distance_metrics))

metrics_dataframe_agglomerative = pd.DataFrame(index=index, columns=clustering_metrics_titles)

for nclusters, distance_metric in itertools.product(clusters_range, distance_metrics):
    algorithm = AgglomerativeClustering(n_clusters=nclusters, affinity=distance_metric, linkage='average')
    agglomerative_predictions = algorithm.fit_predict(dataset_2d)
    
    agglomerative_clustering_labels_mapping[nclusters][distance_metric] = agglomerative_predictions
    
    for metric, metric_title in zip(clustering_metrics, clustering_metrics_titles):
        if metric_title not in ('silhouette_score', 'davies_bouldin_score', 'calinski_harabasz_score'):
            metrics_dataframe_agglomerative.loc[(nclusters, distance_metric), metric_title] = metric(
                labels, agglomerative_predictions)
        else:
            metrics_dataframe_agglomerative.loc[(nclusters, distance_metric), metric_title] = metric(dataset_2d, labels)

metrics_dataframe_agglomerative.index.names = ['nclusters', 'affinity']
metrics_dataframe_agglomerative

Find best nclusters and affinity for Agglomerative Clustering¶

Davies_bouldin_score we have to minimize¶

metrics_dataframe_agglomerative[metrics_min].style.apply(highlight_min)

Other metrics we have to maximize¶

metrics_dataframe_agglomerative[metrics_max].style.apply(highlight_max)

Seems like 7 clusters and cosine are the best for Agglomerative Clustering algorithm

Vizualize clusters for each parameter¶

fig, axs = plt.subplots(nrows=len(clusters_range), ncols=len(distance_metrics), figsize=(100, 120))

idx = 0

for i, data_agg in enumerate(agglomerative_clustering_labels_mapping.items()):
    nclusters, data_dict = data_agg
    for j, data_agg1 in enumerate(data_dict.items()):
        affinity, labels = data_agg1
        colors_mapping = [(item/255.) for item in labels]
        axs[i][j].scatter(dataset_2d[:, 0], dataset_2d[:, 1], c=colors_mapping);
        axs[i][j].set_title('Agglomerative Clustering, number of selected clusters: {}, affinity: {}'.format(
            nclusters, affinity))

	homogeneity_score	silhouette_score	davies_bouldin_score	adjusted_rand_index	calinski_harabasz_score	adjusted_mutual_info	v_measure_score	completeness_score
clustering_algorithm
DBSCAN	0	0.134101	1.97292	-7.97205e-06	408.465	-0.000752646	0	0
AgglomerativeClustering	0.115096	0.134101	1.97292	0.153244	408.465	0.11516	0.11548	0.115867
OPTICS	0.141913	0.134101	1.97292	0.00101	408.465	0.0378084	0.0549031	0.0340353

	homogeneity_score	v_measure_score	completeness_score	adjusted_mutual_info	adjusted_rand_index	calinski_harabasz_score	silhouette_score
clustering_algorithm
DBSCAN	0.000000	0.000000	0.000000	-0.000753	-0.000008	408.464819	0.134101
AgglomerativeClustering	0.115096	0.115480	0.115867	0.115160	0.153244	408.464819	0.134101
OPTICS	0.141913	0.054903	0.034035	0.037808	0.001010	408.464819	0.134101

		homogeneity_score	silhouette_score	davies_bouldin_score	adjusted_rand_index	calinski_harabasz_score	adjusted_mutual_info	v_measure_score	completeness_score
nclusters	affinity
2	euclidean	0.000437997	-0.209239	1.65569	-0.00275536	10.6631	-0.00190102	0.000871643	0.0877418
	l1	0.000218846	-0.209239	1.65569	-0.00138003	10.6631	-0.00123876	0.000436497	0.079989
	l2	0.000437997	-0.209239	1.65569	-0.00275536	10.6631	-0.00190102	0.000871643	0.0877418
	manhattan	0.000218846	-0.209239	1.65569	-0.00138003	10.6631	-0.00123876	0.000436497	0.079989
	cosine	0.104759	-0.209239	1.65569	-0.00717091	10.6631	0.155184	0.170542	0.458369
3	euclidean	0.0158739	-0.209239	1.65569	-0.00356124	10.6631	0.0156289	0.0305198	0.39451
	l1	0.112308	-0.209239	1.65569	0.0019127	10.6631	0.164905	0.180853	0.464125
	l2	0.0158739	-0.209239	1.65569	-0.00356124	10.6631	0.0156289	0.0305198	0.39451
	manhattan	0.112308	-0.209239	1.65569	0.0019127	10.6631	0.164905	0.180853	0.464125
	cosine	0.126093	-0.209239	1.65569	-0.0156452	10.6631	0.167967	0.195924	0.439104
4	euclidean	0.0180388	-0.209239	1.65569	-0.00356868	10.6631	0.015776	0.034525	0.401105
	l1	0.144623	-0.209239	1.65569	0.00675767	10.6631	0.192959	0.220541	0.464238
	l2	0.0180388	-0.209239	1.65569	-0.00356868	10.6631	0.015776	0.034525	0.401105
	manhattan	0.144623	-0.209239	1.65569	0.00675767	10.6631	0.192959	0.220541	0.464238
	cosine	0.192417	-0.209239	1.65569	-0.00463879	10.6631	0.232211	0.268141	0.442145
5	euclidean	0.126127	-0.209239	1.65569	-0.00182424	10.6631	0.170065	0.197367	0.453537
	l1	0.179476	-0.209239	1.65569	-0.024626	10.6631	0.217509	0.255725	0.444616
	l2	0.126127	-0.209239	1.65569	-0.00182424	10.6631	0.170065	0.197367	0.453537
	manhattan	0.179476	-0.209239	1.65569	-0.024626	10.6631	0.217509	0.255725	0.444616
	cosine	0.223445	-0.209239	1.65569	-0.0096054	10.6631	0.250904	0.296308	0.439686
6	euclidean	0.164104	-0.209239	1.65569	-0.00906657	10.6631	0.20218	0.240161	0.447613
	l1	0.180344	-0.209239	1.65569	-0.0251863	10.6631	0.215481	0.256004	0.441026
	l2	0.164104	-0.209239	1.65569	-0.00906657	10.6631	0.20218	0.240161	0.447613
	manhattan	0.180344	-0.209239	1.65569	-0.0251863	10.6631	0.215481	0.256004	0.441026
	cosine	0.255218	-0.209239	1.65569	-0.00159601	10.6631	0.269808	0.322809	0.439098
7	euclidean	0.170644	-0.209239	1.65569	-0.0118611	10.6631	0.200783	0.246049	0.440851
	l1	0.180879	-0.209239	1.65569	-0.0268318	10.6631	0.212677	0.255667	0.435897
	l2	0.170644	-0.209239	1.65569	-0.0118611	10.6631	0.200783	0.246049	0.440851
	manhattan	0.180879	-0.209239	1.65569	-0.0268318	10.6631	0.212677	0.255667	0.435897
	cosine	0.266096	-0.209239	1.65569	-0.00358133	10.6631	0.270759	0.330466	0.435916
8	euclidean	0.207004	-0.209239	1.65569	-0.0302543	10.6631	0.227058	0.280657	0.435674
	l1	0.214953	-0.209239	1.65569	-0.0264911	10.6631	0.236308	0.287495	0.433944
	l2	0.207004	-0.209239	1.65569	-0.0302543	10.6631	0.227058	0.280657	0.435674
	manhattan	0.214953	-0.209239	1.65569	-0.0264911	10.6631	0.236308	0.287495	0.433944
	cosine	0.292581	-0.209239	1.65569	-0.0102035	10.6631	0.283028	0.349638	0.434341
9	euclidean	0.227325	-0.209239	1.65569	-0.037543	10.6631	0.237106	0.297703	0.431199
	l1	0.252884	-0.209239	1.65569	-0.0287025	10.6631	0.26251	0.320768	0.438472
	l2	0.227325	-0.209239	1.65569	-0.037543	10.6631	0.237106	0.297703	0.431199
	manhattan	0.252884	-0.209239	1.65569	-0.0287025	10.6631	0.26251	0.320768	0.438472
	cosine	0.311223	-0.209239	1.65569	-0.0112748	10.6631	0.289587	0.362611	0.434326

		homogeneity_score	v_measure_score	completeness_score	adjusted_mutual_info	adjusted_rand_index	calinski_harabasz_score	silhouette_score
nclusters	affinity
2	euclidean	0.000438	0.000872	0.087742	-0.001901	-0.002755	10.663137	-0.209239
	l1	0.000219	0.000436	0.079989	-0.001239	-0.001380	10.663137	-0.209239
	l2	0.000438	0.000872	0.087742	-0.001901	-0.002755	10.663137	-0.209239
	manhattan	0.000219	0.000436	0.079989	-0.001239	-0.001380	10.663137	-0.209239
	cosine	0.104759	0.170542	0.458369	0.155184	-0.007171	10.663137	-0.209239
3	euclidean	0.015874	0.030520	0.394510	0.015629	-0.003561	10.663137	-0.209239
	l1	0.112308	0.180853	0.464125	0.164905	0.001913	10.663137	-0.209239
	l2	0.015874	0.030520	0.394510	0.015629	-0.003561	10.663137	-0.209239
	manhattan	0.112308	0.180853	0.464125	0.164905	0.001913	10.663137	-0.209239
	cosine	0.126093	0.195924	0.439104	0.167967	-0.015645	10.663137	-0.209239
4	euclidean	0.018039	0.034525	0.401105	0.015776	-0.003569	10.663137	-0.209239
	l1	0.144623	0.220541	0.464238	0.192959	0.006758	10.663137	-0.209239
	l2	0.018039	0.034525	0.401105	0.015776	-0.003569	10.663137	-0.209239
	manhattan	0.144623	0.220541	0.464238	0.192959	0.006758	10.663137	-0.209239
	cosine	0.192417	0.268141	0.442145	0.232211	-0.004639	10.663137	-0.209239
5	euclidean	0.126127	0.197367	0.453537	0.170065	-0.001824	10.663137	-0.209239
	l1	0.179476	0.255725	0.444616	0.217509	-0.024626	10.663137	-0.209239
	l2	0.126127	0.197367	0.453537	0.170065	-0.001824	10.663137	-0.209239
	manhattan	0.179476	0.255725	0.444616	0.217509	-0.024626	10.663137	-0.209239
	cosine	0.223445	0.296308	0.439686	0.250904	-0.009605	10.663137	-0.209239
6	euclidean	0.164104	0.240161	0.447613	0.202180	-0.009067	10.663137	-0.209239
	l1	0.180344	0.256004	0.441026	0.215481	-0.025186	10.663137	-0.209239
	l2	0.164104	0.240161	0.447613	0.202180	-0.009067	10.663137	-0.209239
	manhattan	0.180344	0.256004	0.441026	0.215481	-0.025186	10.663137	-0.209239
	cosine	0.255218	0.322809	0.439098	0.269808	-0.001596	10.663137	-0.209239
7	euclidean	0.170644	0.246049	0.440851	0.200783	-0.011861	10.663137	-0.209239
	l1	0.180879	0.255667	0.435897	0.212677	-0.026832	10.663137	-0.209239
	l2	0.170644	0.246049	0.440851	0.200783	-0.011861	10.663137	-0.209239
	manhattan	0.180879	0.255667	0.435897	0.212677	-0.026832	10.663137	-0.209239
	cosine	0.266096	0.330466	0.435916	0.270759	-0.003581	10.663137	-0.209239
8	euclidean	0.207004	0.280657	0.435674	0.227058	-0.030254	10.663137	-0.209239
	l1	0.214953	0.287495	0.433944	0.236308	-0.026491	10.663137	-0.209239
	l2	0.207004	0.280657	0.435674	0.227058	-0.030254	10.663137	-0.209239
	manhattan	0.214953	0.287495	0.433944	0.236308	-0.026491	10.663137	-0.209239
	cosine	0.292581	0.349638	0.434341	0.283028	-0.010204	10.663137	-0.209239
9	euclidean	0.227325	0.297703	0.431199	0.237106	-0.037543	10.663137	-0.209239
	l1	0.252884	0.320768	0.438472	0.262510	-0.028702	10.663137	-0.209239
	l2	0.227325	0.297703	0.431199	0.237106	-0.037543	10.663137	-0.209239
	manhattan	0.252884	0.320768	0.438472	0.262510	-0.028702	10.663137	-0.209239
	cosine	0.311223	0.362611	0.434326	0.289587	-0.011275	10.663137	-0.209239

	age	sex	cp	trestbps	chol	fbs	restecg	thalach	exang	oldpeak	slope	thal	target
0	63	1	3	145	233	1	0	150	0	2.3	0	1	1
1	37	1	2	130	250	0	1	187	0	3.5	0	2	1
2	41	0	1	130	204	0	0	172	0	1.4	2	2	1
3	56	1	1	120	236	0	1	178	0	0.8	2	2	1
4	57	0	0	120	354	0	1	163	1	0.6	2	2	1

	davies_bouldin_score
clustering_algorithm
DBSCAN	1.972918
AgglomerativeClustering	1.972918
OPTICS	1.972918

			homogeneity_score	silhouette_score	davies_bouldin_score	adjusted_rand_index	calinski_harabasz_score	adjusted_mutual_info	v_measure_score	completeness_score
min_samples	metrics	epsilon
2	euclidean	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	0.00212968	-0.209239	1.65569	0.000188881	10.6631	0.000901936	0.00423619	0.389229
		0.01000	0.0597561	-0.209239	1.65569	0.0281215	10.6631	0.0453195	0.106962	0.509305
		0.10000	0.331754	-0.209239	1.65569	-0.061022	10.6631	0.200227	0.375183	0.431694
	l1	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	0.00212968	-0.209239	1.65569	0.000188881	10.6631	0.000901936	0.00423619	0.389229
		0.01000	0.0401821	-0.209239	1.65569	0.0205699	10.6631	0.0324298	0.074655	0.525432
		0.10000	0.578801	-0.209239	1.65569	0.0384051	10.6631	0.329033	0.518904	0.470241
	l2	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	0.00212968	-0.209239	1.65569	0.000188881	10.6631	0.000901936	0.00423619	0.389229
		0.01000	0.0597561	-0.209239	1.65569	0.0281215	10.6631	0.0453195	0.106962	0.509305
		0.10000	0.331754	-0.209239	1.65569	-0.061022	10.6631	0.200227	0.375183	0.431694
	manhattan	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	0.00212968	-0.209239	1.65569	0.000188881	10.6631	0.000901936	0.00423619	0.389229
		0.01000	0.0401821	-0.209239	1.65569	0.0205699	10.6631	0.0324298	0.074655	0.525432
		0.10000	0.578801	-0.209239	1.65569	0.0384051	10.6631	0.329033	0.518904	0.470241
	cosine	0.00001	0.611106	-0.209239	1.65569	0.00310503	10.6631	0.11063	0.43854	0.341973
		0.00010	0.289466	-0.209239	1.65569	-0.0400928	10.6631	0.19225	0.329548	0.382513
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
	chebyshev	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	0.00212968	-0.209239	1.65569	0.000188881	10.6631	0.000901936	0.00423619	0.389229
		0.01000	0.0685236	-0.209239	1.65569	0.0332633	10.6631	0.0526444	0.120935	0.514338
		0.10000	0.188671	-0.209239	1.65569	-0.0984233	10.6631	0.100187	0.250294	0.371696
3	euclidean	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.309783	-0.209239	1.65569	-0.0438736	10.6631	0.227516	0.370036	0.459385
	l1	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.535455	-0.209239	1.65569	0.0924593	10.6631	0.37282	0.522862	0.510849
	l2	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.309783	-0.209239	1.65569	-0.0438736	10.6631	0.227516	0.370036	0.459385
	manhattan	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.535455	-0.209239	1.65569	0.0924593	10.6631	0.37282	0.522862	0.510849
	cosine	0.00001	0.545079	-0.209239	1.65569	0.00724173	10.6631	0.11938	0.417109	0.337802
		0.00010	0.288982	-0.209239	1.65569	-0.0400973	10.6631	0.192117	0.329132	0.382238
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
	chebyshev	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	0.00285043	-0.209239	1.65569	0.000254821	10.6631	0.00118594	0.00565682	0.366101
		0.10000	0.177531	-0.209239	1.65569	-0.0884698	10.6631	0.12751	0.246054	0.400721
4	euclidean	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.351188	-0.209239	1.65569	0.0374633	10.6631	0.324129	0.430417	0.55581
	l1	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.510729	-0.209239	1.65569	0.209859	10.6631	0.438721	0.549228	0.594005
	l2	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.351188	-0.209239	1.65569	0.0374633	10.6631	0.324129	0.430417	0.55581
	manhattan	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.510729	-0.209239	1.65569	0.209859	10.6631	0.438721	0.549228	0.594005
	cosine	0.00001	0.478999	-0.209239	1.65569	0.0194364	10.6631	0.120729	0.395245	0.336421
		0.00010	0.306394	-0.209239	1.65569	-0.0433273	10.6631	0.203502	0.340899	0.384161
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
	chebyshev	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.205542	-0.209239	1.65569	-0.0569046	10.6631	0.190202	0.285743	0.468578
5	euclidean	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.357622	-0.209239	1.65569	0.0858907	10.6631	0.355875	0.448583	0.601598
	l1	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.505316	-0.209239	1.65569	0.25479	10.6631	0.44343	0.550532	0.604636
	l2	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.357622	-0.209239	1.65569	0.0858907	10.6631	0.355875	0.448583	0.601598
	manhattan	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.505316	-0.209239	1.65569	0.25479	10.6631	0.44343	0.550532	0.604636
	cosine	0.00001	0.418196	-0.209239	1.65569	0.0585298	10.6631	0.13328	0.379441	0.347259
		0.00010	0.303195	-0.209239	1.65569	-0.0432437	10.6631	0.202574	0.338433	0.382939
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
	chebyshev	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.244589	-0.209239	1.65569	-0.0197397	10.6631	0.250736	0.336414	0.538628
6	euclidean	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.357512	-0.209239	1.65569	0.131179	10.6631	0.368412	0.453451	0.619766
	l1	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.419073	-0.209239	1.65569	0.210664	10.6631	0.39217	0.496935	0.610332
	l2	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.357512	-0.209239	1.65569	0.131179	10.6631	0.368412	0.453451	0.619766
	manhattan	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.419073	-0.209239	1.65569	0.210664	10.6631	0.39217	0.496935	0.610332
	cosine	0.00001	0.317186	-0.209239	1.65569	0.057531	10.6631	0.109752	0.327983	0.339542
		0.00010	0.312632	-0.209239	1.65569	-0.039032	10.6631	0.211111	0.345744	0.386701
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
	chebyshev	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.273424	-0.209239	1.65569	0.0273987	10.6631	0.287674	0.368801	0.566364
7	euclidean	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.302881	-0.209239	1.65569	0.136618	10.6631	0.337327	0.407557	0.622796
	l1	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.339161	-0.209239	1.65569	0.156088	10.6631	0.319387	0.430648	0.589723
	l2	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.302881	-0.209239	1.65569	0.136618	10.6631	0.337327	0.407557	0.622796
	manhattan	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.339161	-0.209239	1.65569	0.156088	10.6631	0.319387	0.430648	0.589723
	cosine	0.00001	0.224995	-0.209239	1.65569	0.0681203	10.6631	0.100778	0.273502	0.348671
		0.00010	0.31269	-0.209239	1.65569	-0.0401478	10.6631	0.199504	0.342124	0.377674
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
	chebyshev	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.245809	-0.209239	1.65569	0.046511	10.6631	0.271405	0.341076	0.556918
8	euclidean	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.364015	-0.209239	1.65569	0.157489	10.6631	0.359414	0.446923	0.578735
	l1	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.23904	-0.209239	1.65569	0.105102	10.6631	0.23783	0.338547	0.57998
	l2	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.364015	-0.209239	1.65569	0.157489	10.6631	0.359414	0.446923	0.578735
	manhattan	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.23904	-0.209239	1.65569	0.105102	10.6631	0.23783	0.338547	0.57998
	cosine	0.00001	0.147984	-0.209239	1.65569	0.0464943	10.6631	0.076558	0.207688	0.348151
		0.00010	0.315485	-0.209239	1.65569	-0.0388738	10.6631	0.201754	0.344345	0.379018
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
	chebyshev	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.256338	-0.209239	1.65569	0.0809764	10.6631	0.284938	0.352519	0.56422
9	euclidean	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.287244	-0.209239	1.65569	0.0946173	10.6631	0.286173	0.37246	0.529564
	l1	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.167686	-0.209239	1.65569	0.0826433	10.6631	0.18165	0.263297	0.612579
	l2	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.287244	-0.209239	1.65569	0.0946173	10.6631	0.286173	0.37246	0.529564
	manhattan	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.167686	-0.209239	1.65569	0.0826433	10.6631	0.18165	0.263297	0.612579
	cosine	0.00001	0.106951	-0.209239	1.65569	0.0426508	10.6631	0.0584434	0.163737	0.349084
		0.00010	0.294291	-0.209239	1.65569	-0.0350197	10.6631	0.200848	0.331786	0.380232
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
	chebyshev	0.00001	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00010	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.00100	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.01000	-8.40311e-18	-0.209239	1.65569	0	10.6631	-7.8269e-17	-1.68062e-17	1
		0.10000	0.30465	-0.209239	1.65569	0.112639	10.6631	0.312983	0.389916	0.541462

	age	sex	cp	trestbps	chol	fbs	restecg	thalach	exang	oldpeak	slope	thal	target
0	63	1	3	145	233	1	0	150	0	2.3	0	1	1
1	37	1	2	130	250	0	1	187	0	3.5	0	2	1
2	41	0	1	130	204	0	0	172	0	1.4	2	2	1
3	56	1	1	120	236	0	1	178	0	0.8	2	2	1
4	57	0	0	120	354	0	1	163	1	0.6	2	2	1

	homogeneity_score	silhouette_score	davies_bouldin_score	adjusted_rand_index	calinski_harabasz_score	adjusted_mutual_info	v_measure_score	completeness_score
clustering_algorithm
DBSCAN	5.63966e-16	0.0503427	4.06686	0	11.8638	3.47169e-15	1.12793e-15	1
AgglomerativeClustering	0.0211148	0.0503427	4.06686	0.0321577	11.8638	0.0210758	0.023793	0.0272495
OPTICS	0.102684	0.0503427	4.06686	0.0161423	11.8638	0.0285888	0.0530897	0.0357993

	davies_bouldin_score
clustering_algorithm
DBSCAN	4.066858
AgglomerativeClustering	4.066858
OPTICS	4.066858

			davies_bouldin_score
min_samples	metrics	epsilon
2	euclidean	1e-05	1.655692
		0.0001	1.655692
		0.001	1.655692
		0.01	1.655692
		0.1	1.655692
	l1	1e-05	1.655692
		0.0001	1.655692
		0.001	1.655692
		0.01	1.655692
		0.1	1.655692
	l2	1e-05	1.655692
		0.0001	1.655692
		0.001	1.655692
		0.01	1.655692
		0.1	1.655692
	manhattan	1e-05	1.655692
		0.0001	1.655692
		0.001	1.655692
		0.01	1.655692
		0.1	1.655692
	cosine	1e-05	1.655692
		0.0001	1.655692
		0.001	1.655692
		0.01	1.655692
		0.1	1.655692
	chebyshev	1e-05	1.655692
		0.0001	1.655692
		0.001	1.655692
		0.01	1.655692
		0.1	1.655692
3	euclidean	1e-05	1.655692
		0.0001	1.655692
		0.001	1.655692
		0.01	1.655692
		0.1	1.655692
	l1	1e-05	1.655692
		0.0001	1.655692
		0.001	1.655692
		0.01	1.655692

			homogeneity_score	v_measure_score	completeness_score	adjusted_mutual_info	adjusted_rand_index	calinski_harabasz_score	silhouette_score
min_samples	metrics	epsilon
2	euclidean	1e-05	-0.000000	-0.000000	1.000000	-0.000000	0.000000	10.663137	-0.209239
		0.0001	-0.000000	-0.000000	1.000000	-0.000000	0.000000	10.663137	-0.209239
		0.001	0.002130	0.004236	0.389229	0.000902	0.000189	10.663137	-0.209239
		0.01	0.059756	0.106962	0.509305	0.045320	0.028122	10.663137	-0.209239
		0.1	0.331754	0.375183	0.431694	0.200227	-0.061022	10.663137	-0.209239
	l1	1e-05	-0.000000	-0.000000	1.000000	-0.000000	0.000000	10.663137	-0.209239
		0.0001	-0.000000	-0.000000	1.000000	-0.000000	0.000000	10.663137	-0.209239
		0.001	0.002130	0.004236	0.389229	0.000902	0.000189	10.663137	-0.209239
		0.01	0.040182	0.074655	0.525432	0.032430	0.020570	10.663137	-0.209239
		0.1	0.578801	0.518904	0.470241	0.329033	0.038405	10.663137	-0.209239
	l2	1e-05	-0.000000	-0.000000	1.000000	-0.000000	0.000000	10.663137	-0.209239
		0.0001	-0.000000	-0.000000	1.000000	-0.000000	0.000000	10.663137	-0.209239
		0.001	0.002130	0.004236	0.389229	0.000902	0.000189	10.663137	-0.209239
		0.01	0.059756	0.106962	0.509305	0.045320	0.028122	10.663137	-0.209239
		0.1	0.331754	0.375183	0.431694	0.200227	-0.061022	10.663137	-0.209239
	manhattan	1e-05	-0.000000	-0.000000	1.000000	-0.000000	0.000000	10.663137	-0.209239
		0.0001	-0.000000	-0.000000	1.000000	-0.000000	0.000000	10.663137	-0.209239
		0.001	0.002130	0.004236	0.389229	0.000902	0.000189	10.663137	-0.209239
		0.01	0.040182	0.074655	0.525432	0.032430	0.020570	10.663137	-0.209239
		0.1	0.578801	0.518904	0.470241	0.329033	0.038405	10.663137	-0.209239
	cosine	1e-05	0.611106	0.438540	0.341973	0.110630	0.003105	10.663137	-0.209239
		0.0001	0.289466	0.329548	0.382513	0.192250	-0.040093	10.663137	-0.209239
		0.001	-0.000000	-0.000000	1.000000	-0.000000	0.000000	10.663137	-0.209239
		0.01	-0.000000	-0.000000	1.000000	-0.000000	0.000000	10.663137	-0.209239
		0.1	-0.000000	-0.000000	1.000000	-0.000000	0.000000	10.663137	-0.209239
	chebyshev	1e-05	-0.000000	-0.000000	1.000000	-0.000000	0.000000	10.663137	-0.209239
		0.0001	-0.000000	-0.000000	1.000000	-0.000000	0.000000	10.663137	-0.209239
		0.001	0.002130	0.004236	0.389229	0.000902	0.000189	10.663137	-0.209239
		0.01	0.068524	0.120935	0.514338	0.052644	0.033263	10.663137	-0.209239
		0.1	0.188671	0.250294	0.371696	0.100187	-0.098423	10.663137	-0.209239
3	euclidean	1e-05	-0.000000	-0.000000	1.000000	-0.000000	0.000000	10.663137	-0.209239
		0.0001	-0.000000	-0.000000	1.000000	-0.000000	0.000000	10.663137	-0.209239
		0.001	-0.000000	-0.000000	1.000000	-0.000000	0.000000	10.663137	-0.209239
		0.01	-0.000000	-0.000000	1.000000	-0.000000	0.000000	10.663137	-0.209239
		0.1	0.309783	0.370036	0.459385	0.227516	-0.043874	10.663137	-0.209239
	l1	1e-05	-0.000000	-0.000000	1.000000	-0.000000	0.000000	10.663137	-0.209239
		0.0001	-0.000000	-0.000000	1.000000	-0.000000	0.000000	10.663137	-0.209239
		0.001	-0.000000	-0.000000	1.000000	-0.000000	0.000000	10.663137	-0.209239
		0.01	-0.000000	-0.000000	1.000000	-0.000000	0.000000	10.663137	-0.209239

			homogeneity_score	silhouette_score	davies_bouldin_score	adjusted_rand_index	calinski_harabasz_score	adjusted_mutual_info	v_measure_score	completeness_score
min_samples	metrics	epsilon
2	euclidean	0.00001	0.835575	-0.209239	1.65569	0.0185687	10.6631	0.238611	0.561198	0.422471
		0.00010	0.835575	-0.209239	1.65569	0.0185687	10.6631	0.238611	0.561198	0.422471
		0.00100	0.835575	-0.209239	1.65569	0.0185687	10.6631	0.238611	0.561198	0.422471
		0.01000	0.835575	-0.209239	1.65569	0.0185687	10.6631	0.238611	0.561198	0.422471
		0.10000	0.835575	-0.209239	1.65569	0.0185687	10.6631	0.238611	0.561198	0.422471
	l1	0.00001	0.819782	-0.209239	1.65569	0.0243951	10.6631	0.242293	0.559087	0.424192
		0.00010	0.819782	-0.209239	1.65569	0.0243951	10.6631	0.242293	0.559087	0.424192
		0.00100	0.819782	-0.209239	1.65569	0.0243951	10.6631	0.242293	0.559087	0.424192
		0.01000	0.819782	-0.209239	1.65569	0.0243951	10.6631	0.242293	0.559087	0.424192
		0.10000	0.819782	-0.209239	1.65569	0.0243951	10.6631	0.242293	0.559087	0.424192
	l2	0.00001	0.835575	-0.209239	1.65569	0.0185687	10.6631	0.238611	0.561198	0.422471
		0.00010	0.835575	-0.209239	1.65569	0.0185687	10.6631	0.238611	0.561198	0.422471
		0.00100	0.835575	-0.209239	1.65569	0.0185687	10.6631	0.238611	0.561198	0.422471
		0.01000	0.835575	-0.209239	1.65569	0.0185687	10.6631	0.238611	0.561198	0.422471
		0.10000	0.835575	-0.209239	1.65569	0.0185687	10.6631	0.238611	0.561198	0.422471
	manhattan	0.00001	0.819782	-0.209239	1.65569	0.0243951	10.6631	0.242293	0.559087	0.424192
		0.00010	0.819782	-0.209239	1.65569	0.0243951	10.6631	0.242293	0.559087	0.424192
		0.00100	0.819782	-0.209239	1.65569	0.0243951	10.6631	0.242293	0.559087	0.424192
		0.01000	0.819782	-0.209239	1.65569	0.0243951	10.6631	0.242293	0.559087	0.424192
		0.10000	0.819782	-0.209239	1.65569	0.0243951	10.6631	0.242293	0.559087	0.424192
	cosine	0.00001	0.727347	-0.209239	1.65569	-0.000168506	10.6631	0.0348715	0.462433	0.338973
		0.00010	0.727347	-0.209239	1.65569	-0.000168506	10.6631	0.0348715	0.462433	0.338973
		0.00100	0.727347	-0.209239	1.65569	-0.000168506	10.6631	0.0348715	0.462433	0.338973
		0.01000	0.727347	-0.209239	1.65569	-0.000168506	10.6631	0.0348715	0.462433	0.338973
		0.10000	0.727347	-0.209239	1.65569	-0.000168506	10.6631	0.0348715	0.462433	0.338973
	chebyshev	0.00001	0.837425	-0.209239	1.65569	0.0238625	10.6631	0.244605	0.563881	0.425041
		0.00010	0.837425	-0.209239	1.65569	0.0238625	10.6631	0.244605	0.563881	0.425041
		0.00100	0.837425	-0.209239	1.65569	0.0238625	10.6631	0.244605	0.563881	0.425041
		0.01000	0.837425	-0.209239	1.65569	0.0238625	10.6631	0.244605	0.563881	0.425041
		0.10000	0.837425	-0.209239	1.65569	0.0238625	10.6631	0.244605	0.563881	0.425041
3	euclidean	0.00001	0.72365	-0.209239	1.65569	0.142826	10.6631	0.368579	0.587255	0.494122
		0.00010	0.72365	-0.209239	1.65569	0.142826	10.6631	0.368579	0.587255	0.494122
		0.00100	0.72365	-0.209239	1.65569	0.142826	10.6631	0.368579	0.587255	0.494122
		0.01000	0.72365	-0.209239	1.65569	0.142826	10.6631	0.368579	0.587255	0.494122
		0.10000	0.72365	-0.209239	1.65569	0.142826	10.6631	0.368579	0.587255	0.494122
	l1	0.00001	0.749542	-0.209239	1.65569	0.159165	10.6631	0.390759	0.602697	0.503964
		0.00010	0.749542	-0.209239	1.65569	0.159165	10.6631	0.390759	0.602697	0.503964
		0.00100	0.749542	-0.209239	1.65569	0.159165	10.6631	0.390759	0.602697	0.503964
		0.01000	0.749542	-0.209239	1.65569	0.159165	10.6631	0.390759	0.602697	0.503964

			davies_bouldin_score
min_samples	metrics	epsilon
2	euclidean	1e-05	1.655692
		0.0001	1.655692
		0.001	1.655692
		0.01	1.655692
		0.1	1.655692
	l1	1e-05	1.655692
		0.0001	1.655692
		0.001	1.655692
		0.01	1.655692
		0.1	1.655692
	l2	1e-05	1.655692
		0.0001	1.655692
		0.001	1.655692
		0.01	1.655692
		0.1	1.655692
	manhattan	1e-05	1.655692
		0.0001	1.655692
		0.001	1.655692
		0.01	1.655692
		0.1	1.655692
	cosine	1e-05	1.655692
		0.0001	1.655692
		0.001	1.655692
		0.01	1.655692
		0.1	1.655692
	chebyshev	1e-05	1.655692
		0.0001	1.655692
		0.001	1.655692
		0.01	1.655692
		0.1	1.655692
3	euclidean	1e-05	1.655692
		0.0001	1.655692
		0.001	1.655692
		0.01	1.655692
		0.1	1.655692
	l1	1e-05	1.655692
		0.0001	1.655692
		0.001	1.655692
		0.01	1.655692

	age	sex	cp	trestbps	chol	fbs	restecg	thalach	exang	oldpeak	slope	thal	target
0	63	1	3	145	233	1	0	150	0	2.3	0	1	1
1	37	1	2	130	250	0	1	187	0	3.5	0	2	1
2	41	0	1	130	204	0	0	172	0	1.4	2	2	1
3	56	1	1	120	236	0	1	178	0	0.8	2	2	1
4	57	0	0	120	354	0	1	163	1	0.6	2	2	1

	age	sex	cp	trestbps	chol	fbs	restecg	thalach	exang	oldpeak	slope	thal	target
0	63	1	3	145	233	1	0	150	0	2.3	0	1	1
1	37	1	2	130	250	0	1	187	0	3.5	0	2	1
2	41	0	1	130	204	0	0	172	0	1.4	2	2	1
3	56	1	1	120	236	0	1	178	0	0.8	2	2	1
4	57	0	0	120	354	0	1	163	1	0.6	2	2	1